The overall architecture of POMP is shown in Figure REF .
At each time step, the inputs are the agent pose, i.e. position and orientation, in a known 2D map and a RGB-D frame given by a sensor acquisition. An off-the-shelf object detector is applied to the RGB image, where the corresponding depth of the candidate target proposal is further exploited to obtain the candidate position in the map. Such object-related observation is then passed to the POMCP exploration module that assigns each possible move a reward indicating that a chosen move brings the agent closer to the object.
The policy is learnt online by Monte Carlo simulations and related particle-filter based belief update, therefore it is general and easy-to-deploy in any environment.
Crucially, our approach exploits the model of the environment to consider the sensor's field of view and all the admissible moves of the agent in the area. For our active vision search scenario, such a model can be easily obtained by building a map of the environment to include the position of fixed elements (e.g. walls) but does not need to consider the position of moving objects.
Unlike other RL-based strategies [1]}, [2]}, implicitly encoding such environment knowledge in a data-driven manner, our motion policy explicitly use the knowledge of the environment for the visibility modelling.
Once the target is detected, the robust visual approaching module further localises the target on the map, so that a destination pose of the agent can be determined, i.e. the closest pose with a frontal-facing viewpoint to the target, for the estimation of the shortest path [3]}. A path replanning scheme is proposed in the docking module to be robust to detector failures, such as miss-detections or false positives.
